def test_lr_eq_0_cov_gt_0(self):
# If a vulnerability function loss ratio is 0 and its corresponding CoV
# is > 0, a ValueError should be raised
vuln_content = writetmp(u"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<vulnerabilityModel>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="population"
lossCategory="occupants">
<IML IMT="PGV">0.005 0.007 0.0098 0.0137</IML>
<discreteVulnerability vulnerabilityFunctionID="A"
probabilisticDistribution="LN">
<lossRatio>0.00 0.06 0.18 0.36</lossRatio>
<coefficientsVariation>0.30 0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
</vulnerabilityModel>
</nrml>
""")
with self.assertRaises(ValueError) as ar:
nrml.parse(vuln_content)
self.assertIn(
'It is not valid to define a loss ratio = 0.0 with a '
'corresponding coeff. of variation > 0.0', str(ar.exception))
def validate_nrml(request):
"""
Leverage oq-risklib to check if a given XML text is a valid NRML
:param request:
a `django.http.HttpRequest` object containing the mandatory
parameter 'xml_text': the text of the XML to be validated as NRML
:returns: a JSON object, containing:
* 'valid': a boolean indicating if the provided text is a valid NRML
* 'error_msg': the error message, if any error was found
(None otherwise)
* 'error_line': line of the given XML where the error was found
(None if no error was found or if it was not a
validation error)
"""
xml_text = request.POST.get('xml_text')
if not xml_text:
return HttpResponseBadRequest(
'Please provide the "xml_text" parameter')
xml_file = writetmp(xml_text, suffix='.xml')
try:
nrml.parse(xml_file)
except ExpatError as exc:
return _make_response(error_msg=str(exc),
error_line=exc.lineno,
valid=False)
except Exception as exc:
# get the exception message
exc_msg = exc.args[0]
if isinstance(exc_msg, bytes):
exc_msg = exc_msg.decode('utf-8') # make it a unicode object
elif isinstance(exc_msg, unicode):
pass
else:
# if it is another kind of object, it is not obvious a priori how
# to extract the error line from it
return _make_response(error_msg=unicode(exc_msg),
error_line=None,
valid=False)
# if the line is not mentioned, the whole message is taken
error_msg = exc_msg.split(', line')[0]
# check if the exc_msg contains a line number indication
search_match = re.search(r'line \d+', exc_msg)
if search_match:
error_line = int(search_match.group(0).split()[1])
else:
error_line = None
return _make_response(error_msg=error_msg,
error_line=error_line,
valid=False)
else:
return _make_response(error_msg=None, error_line=None, valid=True)
def test_invalid_vf_pmf(self):
fname = writetmp('''\
<?xml version="1.0" encoding="UTF-8"?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.5">
<vulnerabilityModel id="Vulnerabilidad" assetCategory="MI_AIS" lossCategory="structural">
<description>Vulnerabilidad Microcomponentes</description>
<vulnerabilityFunction id="MI_PTR" dist="PM">
<imls imt="PGA">0.00 0.10 0.20 0.31 0.41 0.51 0.61 0.71 0.82 0.92 1.02 1.12 1.22 1.33 1.43 1.53 </imls>
<probabilities lr="0">1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.000000000017">0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.000024">0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.005">0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.067">0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.25">0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.50">0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.72">0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.86">0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.93">0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0</probabilities>
<probabilities lr="0.97">0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0</probabilities>
<probabilities lr="0.99">0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0</probabilities>
<probabilities lr="0.995">0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0</probabilities>
<probabilities lr="0.998">0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0</probabilities>
<probabilities lr="0.999">0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0</probabilities>
<probabilities lr="1">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1</probabilities>
</vulnerabilityFunction>
<vulnerabilityFunction id="MI_TON" dist="PM">
<imls imt="PGA">0.00 0.10 0.20 0.31 0.41 0.51 0.61 0.71 0.82 0.92 1.02 1.12 1.22 1.33 </imls>
<probabilities lr="0">1 0 0 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.0000317">0 1 0 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.025">0 0 1 0 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.0221">0 0 0 1 0 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.531">0 0 0 0 1 0 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.768">0 0 0 0 0 1 0 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.898">0 0 0 0 0 0 1 0 0 0 0 0 0 0</probabilities>
<probabilities lr="0.958">0 0 0 0 0 0 0 1 0 0 0 0 0 0</probabilities>
<probabilities lr="0.983">0 0 0 0 0 0 0 0 1 0 0 0 0 0</probabilities>
<probabilities lr="0.993">0 0 0 0 0 0 0 0 0 1 0 0 0 0</probabilities>
<probabilities lr="0.997">0 0 0 0 0 0 0 0 0 0 1 0 0 0</probabilities>
<probabilities lr="0.998">0 0 0 0 0 0 0 0 0 0 0 1 0 0</probabilities>
<probabilities lr="0.999">0 0 0 0 0 0 0 0 0 0 0 0 1 0</probabilities>
<probabilities lr="1">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1</probabilities>
</vulnerabilityFunction>
</vulnerabilityModel>
</nrml>''')
with self.assertRaises(ValueError) as ctx:
nrml.parse(fname)
self.assertIn('Wrong number of probabilities (expected 14, got 17)',
str(ctx.exception))
def test_ok(self):
fname = os.path.join(EXAMPLES_DIR, 'consequence-model.xml')
cmodel = nrml.parse(fname)
self.assertEqual(
repr(cmodel),
"<ConsequenceModel structural ds1, ds2, ds3, ds4 tax1>")
# test pickleability
pickle.loads(pickle.dumps(cmodel))
def test(self):
source_model = os.path.join(os.path.dirname(__file__), 'nankai.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(135.68, 35.68), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), None)
imtls = DictArray({'PGV': [20, 40, 80]})
gsim_by_trt = {'Subduction Interface': SiMidorikawa1999SInter()}
hcurves = calc_hazard_curves_ext(groups, s_filter, imtls, gsim_by_trt)
npt.assert_almost_equal([0.91149953, 0.12548556, 0.00177583],
hcurves['PGV'][0])
def setUp(self):
d = os.path.dirname(os.path.dirname(__file__))
source_model = os.path.join(d, 'source_model/multi-point-source.xml')
[self.sources] = nrml.parse(
source_model,
SourceConverter(investigation_time=50., rupture_mesh_spacing=2.))
self.site = Site(Point(0.1, 0.1), 800, True, z1pt0=100., z2pt5=1.)
self.imt = PGA()
self.iml = 0.1
self.truncation_level = 1
self.trt = 'Stable Continental Crust'
self.gsims = {self.trt: Campbell2003()}
def test(self):
source_model = os.path.join(os.path.dirname(__file__), 'nankai.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(135.68, 35.68), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
imtls = DictArray({'PGV': [20, 40, 80]})
gsim_by_trt = {'Subduction Interface': SiMidorikawa1999SInter()}
hcurves = calc_hazard_curves(groups, s_filter, imtls, gsim_by_trt)
npt.assert_almost_equal(
[1.11315443e-01, 3.92180097e-03, 3.02064427e-05],
hcurves['PGV'][0])
def test_one_taxonomy_many_imts(self):
# Should raise a ValueError if a vulnerabilityFunctionID is used for
# multiple IMTs.
# In this test input, we've defined two functions in separate sets
# with the same ID and different IMTs.
vuln_content = writetmp(u"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<vulnerabilityModel>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="population"
lossCategory="occupants">
<IML IMT="PGA">0.005 0.007 0.0098 0.0137</IML>
<discreteVulnerability vulnerabilityFunctionID="A"
probabilisticDistribution="LN">
<lossRatio>0.01 0.06 0.18 0.36</lossRatio>
<coefficientsVariation>0.30 0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="population"
lossCategory="occupants">
<IML IMT="MMI">0.005 0.007 0.0098 0.0137</IML>
<discreteVulnerability vulnerabilityFunctionID="A"
probabilisticDistribution="LN">
<lossRatio>0.01 0.06 0.18 0.36</lossRatio>
<coefficientsVariation>0.30 0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
</vulnerabilityModel>
</nrml>
""")
with self.assertRaises(InvalidFile) as ar:
nrml.parse(vuln_content)
self.assertIn('Duplicated vulnerabilityFunctionID: A',
str(ar.exception))
def test_missing_maxIML(self):
vuln_content = writetmp(u"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns:gml="http://www.opengis.net/gml"
xmlns="http://openquake.org/xmlns/nrml/0.4">
<fragilityModel format="continuous">
<description>Fragility for test</description>
<limitStates>LS1 LS2</limitStates>
<ffs type="lognormal">
<taxonomy>RC</taxonomy>
<IML IMT="PGA" minIML="9.9" imlUnit="g"/>
<ffc ls="LS1">
<params mean="0.2" stddev="0.05" />
</ffc>
<ffc ls="LS2">
<params mean="0.35" stddev="0.10" />
</ffc>
</ffs>
</fragilityModel>
</nrml>""")
with self.assertRaises(KeyError) as ar:
nrml.parse(vuln_content)
self.assertIn("node IML: 'maxIML', line 9", str(ar.exception))
def get_composite_source_model(oq):
"""
:param oq: :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns: a `class:`openquake.commonlib.source.CompositeSourceModel`
"""
[src_group] = nrml.parse(
oq.inputs["source_model"],
SourceConverter(oq.investigation_time, oq.rupture_mesh_spacing))
source_models = []
gsim_lt = readinput.get_gsim_lt(oq, [DEFAULT_TRT])
smlt = readinput.get_source_model_lt(oq)
for sm in smlt.gen_source_models(gsim_lt):
sg = copy.copy(src_group)
sg.id = sm.ordinal
sm.src_groups = [sg]
sg.sources = [sg[0].new(sm.ordinal, sm.name)]
source_models.append(sm)
return source.CompositeSourceModel(gsim_lt, smlt, source_models)
def test(self):
d = os.path.dirname(os.path.dirname(__file__))
source_model = os.path.join(d, 'source_model/multi-point-source.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(0.1, 0.1), 800, True, z1pt0=100., z2pt5=1.)
sitecol = SiteCollection([site])
imtls = DictArray({'PGA': [0.01, 0.02, 0.04, 0.08, 0.16]})
gsim_by_trt = {'Stable Continental Crust': Campbell2003()}
hcurves = calc_hazard_curves(groups, sitecol, imtls, gsim_by_trt)
expected = [0.99999778, 0.9084039, 0.148975348,
0.0036909656, 2.76326e-05]
npt.assert_almost_equal(hcurves['PGA'][0], expected)
# splitting in point sources
[[mps1, mps2]] = groups
psources = list(mps1) + list(mps2)
hcurves = calc_hazard_curves(psources, sitecol, imtls, gsim_by_trt)
npt.assert_almost_equal(hcurves['PGA'][0], expected)
def test(self):
d = os.path.dirname(os.path.dirname(__file__))
source_model = os.path.join(d, 'source_model/multi-point-source.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(0.1, 0.1), 800, True, z1pt0=100., z2pt5=1.)
sitecol = SiteCollection([site])
imtls = DictArray({'PGA': [0.01, 0.02, 0.04, 0.08, 0.16]})
gsim_by_trt = {'Stable Continental Crust': Campbell2003()}
hcurves = calc_hazard_curves(groups, sitecol, imtls, gsim_by_trt)
expected = [0.99999778, 0.9084039, 0.148975348,
0.0036909656, 2.76326e-05]
npt.assert_almost_equal(hcurves['PGA'][0], expected)
# splitting in point sources
[[mps1, mps2]] = groups
psources = list(mps1) + list(mps2)
hcurves = calc_hazard_curves(psources, sitecol, imtls, gsim_by_trt)
npt.assert_almost_equal(hcurves['PGA'][0], expected)
def test_different_levels_ok(self):
# the same IMT can appear with different levels in different
# vulnerability functions
vuln_content = writetmp(u"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<vulnerabilityModel>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="population"
lossCategory="occupants">
<IML IMT="PGA">0.005 0.007 0.0098 0.0137</IML>
<discreteVulnerability vulnerabilityFunctionID="RC/A"
probabilisticDistribution="LN">
<lossRatio>0.01 0.06 0.18 0.36</lossRatio>
<coefficientsVariation>0.30 0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
<discreteVulnerabilitySet vulnerabilitySetID="PAGER"
assetCategory="population"
lossCategory="occupants">
<IML IMT="PGA">0.004 0.008 0.037</IML>
<discreteVulnerability vulnerabilityFunctionID="RC/B"
probabilisticDistribution="LN">
<lossRatio>0.01 0.06 0.18</lossRatio>
<coefficientsVariation>0.30 0.30 0.30
</coefficientsVariation>
</discreteVulnerability>
</discreteVulnerabilitySet>
</vulnerabilityModel>
</nrml>
""")
vfs = nrml.parse(vuln_content)
assert_almost_equal(vfs['PGA', 'RC/A'].imls,
numpy.array([0.005, 0.007, 0.0098, 0.0137]))
assert_almost_equal(vfs['PGA', 'RC/B'].imls,
numpy.array([0.004, 0.008, 0.037]))
def test_case_17(self): # oversampling and save_ruptures
expected = [
'hazard_curve-mean.csv',
'hazard_curve-rlz-001.csv',
'hazard_curve-rlz-002.csv',
'hazard_curve-rlz-003.csv',
'hazard_curve-rlz-004.csv',
]
# test --hc functionality, i.e. that the ruptures are read correctly
out = self.run_calc(case_17.__file__, 'job.ini,job.ini', exports='csv')
fnames = out['hcurves', 'csv']
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
# check that a single rupture file is exported even if there are
# several collections
[fname] = export(('ruptures', 'xml'), self.calc.datastore)
self.assertEqualFiles('expected/ses.xml', fname)
# check that the exported file is parseable
rupcoll = nrml.parse(fname, RuptureConverter(1))
self.assertEqual(list(rupcoll), [1]) # one group
self.assertEqual(len(rupcoll[1]), 3) # three EBRuptures
def test_wrong_files(self):
# missing lossCategory
with self.assertRaises(KeyError) as ctx:
nrml.parse(self.wrong_csq_model_1)
self.assertIn("node consequenceModel: 'lossCategory', line 3",
str(ctx.exception))
# missing loss state
with self.assertRaises(ValueError) as ctx:
nrml.parse(self.wrong_csq_model_2)
self.assertIn(
"node consequenceFunction: Expected 4 limit"
" states, got 3, line 9", str(ctx.exception))
# inverted loss states
with self.assertRaises(ValueError) as ctx:
nrml.parse(self.wrong_csq_model_3)
self.assertIn("node params: Expected 'ds3', got 'ds4', line 12",
str(ctx.exception))
def get_risk_models(oqparam, kind=None):
"""
:param oqparam:
an OqParam instance
:param kind:
vulnerability|vulnerability_retrofitted|fragility|consequence;
if None it is extracted from the oqparam.file_type attribute
:returns:
a dictionary taxonomy -> loss_type -> function
"""
kind = kind or oqparam.file_type
rmodels = {}
for key in sorted(oqparam.inputs):
mo = re.match('(occupants|%s)_%s$' % (COST_TYPE_REGEX, kind), key)
if mo:
key_type = mo.group(1) # the cost_type in the key
# can be occupants, structural, nonstructural, ...
rmodel = nrml.parse(oqparam.inputs[key])
rmodels[key_type] = rmodel
if rmodel.lossCategory is None: # NRML 0.4
continue
cost_type = str(rmodel.lossCategory)
rmodel_kind = rmodel.__class__.__name__
kind_ = kind.replace('_retrofitted', '') # strip retrofitted
if not rmodel_kind.lower().startswith(kind_):
raise ValueError('Error in the file "%s_file=%s": is '
'of kind %s, expected %s' %
(key, oqparam.inputs[key], rmodel_kind,
kind.capitalize() + 'Model'))
if cost_type != key_type:
raise ValueError(
'Error in the file "%s_file=%s": lossCategory is of type '
'"%s", expected "%s"' %
(key, oqparam.inputs[key], rmodel.lossCategory, key_type))
rdict = collections.defaultdict(dict)
if kind == 'fragility':
limit_states = []
for loss_type, fm in sorted(rmodels.items()):
# build a copy of the FragilityModel with different IM levels
newfm = fm.build(oqparam.continuous_fragility_discretization,
oqparam.steps_per_interval)
for (imt, taxo), ffl in newfm.items():
if not limit_states:
limit_states.extend(fm.limitStates)
# we are rejecting the case of loss types with different
# limit states; this may change in the future
assert limit_states == fm.limitStates, (limit_states,
fm.limitStates)
rdict[taxo][loss_type] = ffl
# TODO: see if it is possible to remove the attribute
# below, used in classical_damage
ffl.steps_per_interval = oqparam.steps_per_interval
oqparam.limit_states = [str(ls) for ls in limit_states]
elif kind == 'consequence':
rdict = rmodels
else: # vulnerability
cl_risk = oqparam.calculation_mode in ('classical', 'classical_risk')
# only for classical_risk reduce the loss_ratios
# to make sure they are strictly increasing
for loss_type, rm in rmodels.items():
for (imt, taxo), rf in rm.items():
rdict[taxo][loss_type] = (rf.strictly_increasing()
if cl_risk else rf)
return rdict
def test_invalid_srcs_weights_length(self):
fname = writetmp('''\
<?xml version="1.0" encoding="utf-8"?>
<nrml
xmlns="http://openquake.org/xmlns/nrml/0.5"
xmlns:gml="http://www.opengis.net/gml"
>
<sourceModel
name="Classical Hazard QA Test, Case 1 source model"
>
<sourceGroup
name="group 1"
tectonicRegion="active shallow crust"
srcs_weights="0.5 0.5"
>
<pointSource
id="1"
name="point source"
>
<pointGeometry>
<gml:Point>
<gml:pos>
0.0000000E+00 0.0000000E+00
</gml:pos>
</gml:Point>
<upperSeismoDepth>
0.0000000E+00
</upperSeismoDepth>
<lowerSeismoDepth>
1.0000000E+01
</lowerSeismoDepth>
</pointGeometry>
<magScaleRel>
PeerMSR
</magScaleRel>
<ruptAspectRatio>
1.0000000E+00
</ruptAspectRatio>
<incrementalMFD
binWidth="1.0000000E+00"
minMag="4.0000000E+00"
>
<occurRates>
1.0000000E+00
</occurRates>
</incrementalMFD>
<nodalPlaneDist>
<nodalPlane dip="90" probability="1" rake="0" strike="0"/>
</nodalPlaneDist>
<hypoDepthDist>
<hypoDepth depth="4" probability="1"/>
</hypoDepthDist>
</pointSource>
</sourceGroup>
</sourceModel>
</nrml>
''')
converter = SourceConverter(50., 1., 10, 0.1, 10.)
with self.assertRaises(ValueError) as ctx:
parse(fname, converter)
self.assertEqual('There are 2 srcs_weights but 1 source(s)',
str(ctx.exception))
